论文标题
用于变异的蒙特卡洛模拟的自回归神经jastrow ansatz
Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
论文作者
论文摘要
从Slater决定因素进行的直接采样与自回归深度神经网络相结合，作为jastrow因子，成为一个完全自动回归的Slater-Jastrow Ansatz，用于变异量子蒙特卡洛，这允许进行无关的采样。消除自相关时间会导致具有可证明的立方缩放的随机算法（具有潜在的大型预成绩），即生产不相关样品的操作数量，并计算出$ \ \ \ \级{O}（O}（o}（n_s^3））$ n_s $ n_s $ n _s的局部能量尺度。该实施是在方格上的二维$ T-V $模型上进行的，以实现为基准。
Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a potentially large prefactor), i.e. the number of operations for producing an uncorrelated sample and for calculating the local energy scales like $\mathcal{O}(N_s^3)$ with the number of orbitals $N_s$. The implementation is benchmarked on the two-dimensional $t-V$ model of spinless fermions on the square lattice.
